Knowledge Graph Embeddings: Representation Learning for Graph Data

Knowledge graphs are powerful tools for representing complex relational data, but traditional symbolic structures struggle with scalability and machine learning tasks. Representation learning solves this by mapping entities and relations into continuous vector spaces. In this course, you will master the fundamental concepts and mathematical techniques behind knowledge graph embeddings. You will move from understanding basic graph structures to exploring translation, tensor factorization, and neural-network-based embedding models that power modern data workflows. What you'll learn: 1. Understand the foundational architecture of knowledge graphs, including entities, relations, and semantic triples. 2. Explore translation-based models to represent relational distances in vector space. 3. Master factorization-based techniques for capturing symmetric and asymmetric semantic patterns. 4. Learn how neural network approaches extract deep structural features from graph data. 5. Apply graph embeddings to modern workflows, including vector databases and retrieval-augmented generation systems. 6. Evaluate embedding quality using standard link prediction and entity resolution metrics. The course begins with foundational definitions of graph structures and semantic triples before guiding you through the mathematical intuition of various embedding algorithms. You will then study how these vector representations are evaluated and integrated into modern machine learning pipelines through comprehensive written explanations and conceptual exercises. This course is designed for beginner data scientists, software engineers, and machine learning enthusiasts who want to understand graph-based representation learning. No prior experience with graph embeddings is required, though a basic familiarity with algebra is helpful. Start learning how to transform complex relational data into actionable machine learning features today.